Oracle Database Advanced Querying (2016)

1 Oracle Database Advanced Querying Zohar Elkayam CTO, Brillix Zohar@Brillix.co.il www.realdbamagic.com Twitter: @realmgic

2 Who am I? • Zohar Elkayam, CTO at Brillix • Programmer, DBA, team leader, database trainer, public speaker, and a senior consultant for over 18 years • Oracle ACE Associate • Part of ilOUG – Israel Oracle User Group • Blogger – www.realdbamagic.com and www.ilDBA.co.il

3 About Brillix • We offer complete, integrated end-to-end solutions based on best-of-breed innovations in database, security and big data technologies • We provide complete end-to-end 24x7 expert remote database services • We offer professional customized on-site trainings, delivered by our top-notch world recognized instructors

5 Agenda • Aggregative and advanced grouping options • Analytic functions, ranking and pagination • Hierarchical and recursive queries • Regular Expressions • Oracle 12c new rows pattern matching • XML and JSON handling with SQL • Oracle 12c (12.1 + 12.2) new features • SQL Developer Command Line tool (if time allows)

6 Our Goal Today • Learning new SQL techniques • We will not expert everything • Getting to know new features (12cR1 and 12cR2) • This is a starting point – don’t be afraid to try

7 The REAL Agenda •‫בסיום‬‫בהודעת‬ ‫משוב‬ ‫טופס‬ ‫אליכם‬ ‫יישלח‬ ‫הסמינר‬ ‫יום‬ SMS,‫דעתכם‬ ‫חוות‬ ‫את‬ ‫לקבל‬ ‫נשמח‬. ‫יום‬ ‫מידי‬ ‫יוגרל‬ ‫המשוב‬ ‫ממלאי‬ ‫בין‬‫טאבלט‬! 10:30-10:45‫הפסקה‬ 12:30-13:30‫משתתפ‬ ‫לכל‬ ‫צהריים‬ ‫ארוחת‬‫המלון‬ ‫בגן‬ ‫הכנס‬ ‫י‬ 15:00-15:15‫הפנים‬ ‫קבלת‬ ‫במתחם‬ ‫מתוקה‬ ‫הפסקה‬ 16:30‫הביתה‬ ‫הולכים‬(‫ל‬ ‫או‬-MySQL User Group Meetup ‫במלון‬ ‫כאן‬ ‫הכנס‬ ‫אחרי‬ ‫מיד‬ ‫שיערך‬)

8 ‫אודות‬Oracle SQL–‫מתקדמות‬ ‫יכולות‬ •‫הספר‬"Oracle SQL–‫יכולות‬ ‫מתקדמות‬,‫לשולף‬ ‫מדריך‬ ‫המהיר‬"‫בשנת‬ ‫פורסם‬2011 •‫ה‬ ‫ספר‬ ‫זה‬-SQL‫הראשון‬‫והיחיד‬ ‫ועד‬ ‫מתחילתו‬ ‫בעברית‬ ‫שנכתב‬ ‫סופו‬ •‫ידי‬ ‫על‬ ‫נכתב‬ ‫הספר‬‫דיוויס‬ ‫עמיאל‬ ‫שלי‬ ‫טכנית‬ ‫עריכה‬ ‫ועבר‬

9 SQL‫מתקדם‬–‫פרקטיים‬ ‫ויישומים‬ ‫טכניקות‬ •‫הספר‬"SQL‫מתקדם‬–‫טכניקות‬ ‫פרקטיים‬ ‫ויישומים‬"‫חדש‬ ‫ספר‬ ‫הוא‬ ‫ידי‬ ‫על‬ ‫השנה‬ ‫שפורסם‬‫קדם‬ ‫רם‬ •‫כ‬ ‫מכיל‬ ‫הספר‬-100‫בעיות‬SQL ‫ופתרונן‬ ‫מורכבות‬ •‫אונליין‬ ‫בגרסת‬ ‫גם‬ ‫קיים‬ •‫לפרטים‬:http://ramkedem.com/

10 ANSI SQL • SQL was invented in 1970 by Dr. E. F. Codd • Each vendor had its own flavor of SQL • Standardized by ASNI since 1986 • Current stable standard is ANSI SQL:2011/2008 • Oracle 11g is compliant to SQL:2008 • Oracle 12c is fully compliant to CORE SQL:2011

11 Queries • In this seminar we will only talk about queries

Group Functions More than just group by…

13 Group Function and SQL • Using SQL for aggregation: – Group functions basics – The CUBE and ROLLUP extensions to the GROUP BY clause – The GROUPING functions – The GROUPING SETS expression • Working with composite columns • Using concatenated groupings

14 Basics • Group functions will return a single row for each group • The group by clause groups rows together and allows group functions to be applied • Common group functions: SUM, MIN, MAX, AVG, etc.

15 Group Functions Syntax SELECT [column,] group_function(column). . . FROM table [WHERE condition] [GROUP BY group_by_expression] [ORDER BY column]; SELECT AVG(salary), STDDEV(salary), COUNT(commission_pct),MAX(hire_date) FROM hr.employees WHERE job_id LIKE 'SA%';

16 SELECT department_id, job_id, SUM(salary), COUNT(employee_id) FROM hr.employees GROUP BY department_id, job_id Order by department_id; The GROUP BY Clause SELECT [column,] group_function(column) FROM table [WHERE condition] [GROUP BY group_by_expression] [ORDER BY column];

17 The HAVING Clause • Use the HAVING clause to specify which groups are to be displayed • You further restrict the groups on the basis of a limiting condition SELECT [column,] group_function(column)... FROM table [WHERE condition] [GROUP BY group_by_expression] [HAVING having_expression] [ORDER BY column];

18 GROUP BY Using ROLLUP and CUBE • Use ROLLUP or CUBE with GROUP BY to produce superaggregate rows by cross-referencing columns • ROLLUP grouping produces a result set containing the regular grouped rows and the subtotal and grand total values • CUBE grouping produces a result set containing the rows from ROLLUP and cross-tabulation rows

19 Using the ROLLUP Operator • ROLLUP is an extension of the GROUP BY clause • Use the ROLLUP operation to produce cumulative aggregates, such as subtotals SELECT [column,] group_function(column). . . FROM table [WHERE condition] [GROUP BY [ROLLUP] group_by_expression] [HAVING having_expression]; [ORDER BY column];

20 Using the ROLLUP Operator: Example SELECT department_id, job_id, SUM(salary) FROM hr.employees WHERE department_id < 60 GROUP BY ROLLUP(department_id, job_id); 1 2 3 Total by DEPARTMENT_ID and JOB_ID Total by DEPARTMENT_ID Grand total

21 Using the CUBE Operator • CUBE is an extension of the GROUP BY clause • You can use the CUBE operator to produce cross- tabulation values with a single SELECT statement SELECT [column,] group_function(column)... FROM table [WHERE condition] [GROUP BY [CUBE] group_by_expression] [HAVING having_expression] [ORDER BY column];

22 SELECT department_id, job_id, SUM(salary) FROM hr.employees WHERE department_id < 60 GROUP BY CUBE (department_id, job_id); . . . Using the CUBE Operator: Example . . . 1 2 3 4 Grand total Total by JOB_ID Total by DEPARTMENT_ID and JOB_ID Total by DEPARTMENT_ID

23 SELECT [column,] group_function(column) .. , GROUPING(expr) FROM table [WHERE condition] [GROUP BY [ROLLUP][CUBE] group_by_expression] [HAVING having_expression] [ORDER BY column]; Working with the GROUPING Function • The GROUPING function: – Is used with the CUBE or ROLLUP operator – Is used to find the groups forming the subtotal in a row – Is used to differentiate stored NULL values from NULL values created by ROLLUP or CUBE – Returns 0 or 1

24 SELECT department_id DEPTID, job_id JOB, SUM(salary), GROUPING(department_id) GRP_DEPT, GROUPING(job_id) GRP_JOB FROM hr.employees WHERE department_id < 50 GROUP BY ROLLUP(department_id, job_id); Working with the GROUPING: Example 1 2 3

25 Working with GROUPING_ID Function • Extension to the GROUPING function • GROUPING_ID returns a number corresponding to the GROUPING bit vector associated with a row • Useful for understanding what level the row is aggregated at and filtering those rows

26 GROUPING_ID Function Example SELECT department_id DEPTID, job_id JOB, SUM(salary), GROUPING_ID(department_id,job_id) GRP_ID FROM hr.employees WHERE department_id < 40 GROUP BY CUBE(department_id, job_id); DEPTID JOB SUM(SALARY) GRP_ID ---------- ---------- ----------- ---------- 48300 3 MK_MAN 13000 2 MK_REP 6000 2 PU_MAN 11000 2 AD_ASST 4400 2 PU_CLERK 13900 2 10 4400 1 10 AD_ASST 4400 0 20 19000 1 20 MK_MAN 13000 0 20 MK_REP 6000 0 30 24900 1 30 PU_MAN 11000 0 30 PU_CLERK 13900 0

27 Working with GROUP_ID Function • GROUP_ID distinguishes duplicate groups resulting from a GROUP BY specification • A Unique group will be assigned 0, the non unique will be assigned 1 to n-1 for n duplicate groups • Useful in filtering out duplicate groupings from the query result

28 GROUP_ID Function Example SELECT department_id DEPTID, job_id JOB, SUM(salary), GROUP_ID() UNIQ_GRP_ID FROM hr.employees WHERE department_id < 40 GROUP BY department_id, CUBE(department_id, job_id); DEPTID JOB SUM(SALARY) UNIQ_GRP_ID ---------- ---------- ----------- ----------- 10 AD_ASST 4400 0 20 MK_MAN 13000 0 20 MK_REP 6000 0 30 PU_MAN 11000 0 30 PU_CLERK 13900 0 10 AD_ASST 4400 1 20 MK_MAN 13000 1 20 MK_REP 6000 1 30 PU_MAN 11000 1 30 PU_CLERK 13900 1 10 4400 0 20 19000 0 30 24900 0 10 4400 1 20 19000 1 30 24900 1

29 GROUPING SETS • The GROUPING SETS syntax is used to define multiple groupings in the same query. • All groupings specified in the GROUPING SETS clause are computed and the results of individual groupings are combined with a UNION ALL operation. • Grouping set efficiency: – Only one pass over the base table is required. – There is no need to write complex UNION statements. – The more elements GROUPING SETS has, the greater the performance benefit.

31 SELECT department_id, job_id, manager_id, AVG(salary) FROM hr.employees GROUP BY GROUPING SETS ((department_id,job_id), (job_id,manager_id)); GROUPING SETS: Example . . . 1 2

33 Composite Columns • A composite column is a collection of columns that are treated as a unit. ROLLUP (a,(b,c), d) • Use parentheses within the GROUP BY clause to group columns, so that they are treated as a unit while computing ROLLUP or CUBE operators. • When used with ROLLUP or CUBE, composite columns require skipping aggregation across certain levels.

35 SELECT department_id, job_id, manager_id, SUM(salary) FROM hr.employees GROUP BY ROLLUP( department_id,(job_id, manager_id)); Composite Columns: Example 1 2 3 4

37 Concatenated Groupings • Concatenated groupings offer a concise way to generate useful combinations of groupings. • To specify concatenated grouping sets, you separate multiple grouping sets, ROLLUP, and CUBE operations with commas so that the Oracle server combines them into a single GROUP BY clause. • The result is a cross-product of groupings from each GROUPING SET. GROUP BY GROUPING SETS(a, b), GROUPING SETS(c, d)

38 SELECT department_id, job_id, manager_id, SUM(salary) FROM hr.employees GROUP BY department_id, ROLLUP(job_id), CUBE(manager_id); Concatenated Groupings: Example … … … 1 3 4 5 6 2 7 … …

Analytic Functions Let’s analyze our data!

40 Overview of SQL for Analysis and Reporting • Oracle has enhanced SQL's analytical processing capabilities by introducing a new family of analytic SQL functions. • These analytic functions enable you to calculate and perform: – Rankings and percentiles – Pivoting operations – Moving window calculations – LAG/LEAD analysis – FIRST/LAST analysis – Linear regression statistics

41 Why Use Analytic Functions? • Ability to see one row from another row in the results • Avoid self-join queries • Summary data in detail rows • Slice and dice within the results

42 Using the Analytic Functions Function type Used for Ranking Calculating ranks, percentiles, and n-tiles of the values in a result set Windowing Calculating cumulative and moving aggregates, works with functions such as SUM, AVG, MIN, and so on Reporting Calculating shares such as market share, works with functions such as SUM, AVG, MIN, MAX, COUNT, VARIANCE, STDDEV, RATIO_TO_REPORT, and so on LAG/LEAD Finding a value in a row or a specified number of rows from a current row FIRST/LAST First or last value in an ordered group Linear Regression Calculating linear regression and other statistics

43 Concepts Used in Analytic Functions • Result set partitions: These are created and available to any aggregate results such as sums and averages. The term “partitions” is unrelated to the table partitions feature. • Window: For each row in a partition, you can define a sliding window of data, which determines the range of rows used to perform the calculations for the current row. • Current row: Each calculation performed with an analytic function is based on a current row within a partition. It serves as the reference point determining the start and end of the window.

45 Reporting Functions • We can use aggregative/group functions as analytic functions (i.e. SUM, AVG, MIN, MAX, COUNT etc.) • Each row will get the aggregative value for a given partition without the need for group by clause so we can have multiple group by’s on the same row • Getting the raw data along with the aggregated value • Use Order By to get cumulative aggregations

46 Reporting Functions Examples SELECT last_name, salary, ROUND(AVG(salary) OVER (PARTITION BY department_id),2), COUNT(*) OVER (PARTITION BY manager_id), SUM(salary) OVER (PARTITION BY department_id ORDER BY salary), MAX(salary) OVER () FROM hr.employees;

48 Using the Ranking Functions • A ranking function computes the rank of a record compared to other records in the data set based on the values of a set of measures. The types of ranking function are: – RANK and DENSE_RANK functions – PERCENT_RANK function – ROW_NUMBER function – NTILE function – CUME_DIST function

49 Working with the RANK Function • The RANK function calculates the rank of a value in a group of values, which is useful for top-N and bottom-N reporting. • For example, you can use the RANK function to find the top ten products sold in Boston last year. • When using the RANK function, ascending is the default sort order, which you can change to descending. • Rows with equal values for the ranking criteria receive the same rank. • Oracle Database then adds the number of tied rows to the tied rank to calculate the next rank. RANK ( ) OVER ( [query_partition_clause] order_by_clause )

50 Using the RANK Function: Example SELECT department_id, last_name, salary, RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) "Rank" FROM employees WHERE department_id = 60 ORDER BY department_id, "Rank", salary;

51 Per-Group Ranking • The RANK function can be made to operate within groups - that is, the rank gets reset whenever the group changes • This is accomplished with the PARTITION BY clause • The group expressions in the PARTITION BY sub- clause divide the data set into groups within which RANK operates • For example: to rank products within each channel by their dollar sales, you could issue a statement similar to the one in the next slide.

52 Per-Group Ranking: Example SELECT channel_desc, calendar_month_desc, TO_CHAR(SUM(amount_sold), '9,999,999,999') SALES$, RANK() OVER (PARTITION BY channel_desc ORDER BY SUM(amount_sold) DESC) AS RANK_BY_CHANNEL FROM sales, products, customers, times, channels WHERE sales.prod_id = products.prod_id AND sales.cust_id = customers.cust_id AND sales.time_id = times.time_id AND sales.channel_Id = channels.channel_id AND times.calendar_month_desc IN ('2000-08', '2000-09', '2000- 10', '2000-11') AND channels.channel_desc IN ('Direct Sales', 'Internet') GROUP BY channel_desc, calendar_month_desc;

53 RANK and DENSE_RANK Functions: Example SELECT department_id, last_name, salary, RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) "Rank", DENSE_RANK() over (partition by department_id ORDER BY salary DESC) "Drank" FROM employees WHERE department_id = 60 ORDER BY department_id, last_name, salary DESC, "Rank" DESC; DENSE_RANK ( ) OVER ([query_partition_clause] order_by_clause)

54 Per-Cube and Rollup Group Ranking SELECT channel_desc, country_iso_code, TO_CHAR(SUM(amount_sold), '9,999,999,999')SALES$, RANK() OVER (PARTITION BY GROUPING_ID(channel_desc, country_iso_code) ORDER BY SUM(amount_sold) DESC) AS RANK_PER_GROUP FROM sales, customers, times, channels, countries WHERE sales.time_id = times.time_id AND sales.cust_id=customers.cust_id AND sales.channel_id = channels.channel_id AND channels.channel_desc IN ('Direct Sales', 'Internet') AND times.calendar_month_desc='2000-09' AND country_iso_code IN ('GB', 'US', 'JP') GROUP BY CUBE(channel_desc, country_iso_code);

55 Using the PERCENT_RANK Function • Uses rank values in its numerator and returns the percent rank of a value relative to a group of values • PERCENT_RANK of a row is calculated as follows: • The range of values returned by PERCENT_RANK is 0 to 1, inclusive. The first row in any set has a PERCENT_RANK of 0. The return value is NUMBER. Its syntax is: (rank of row in its partition - 1) / (number of rows in the partition - 1) PERCENT_RANK () OVER ([query_partition_clause] order_by_clause)

56 Using the PERCENT_RANK Function: Example SELECT department_id, last_name, salary, PERCENT_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) AS pr FROM hr.employees ORDER BY department_id, pr, salary;

57 Working with the ROW_NUMBER Function • The ROW_NUMBER function calculates a sequential number of a value in a group of values. • When using the ROW_NUMBER function, ascending is the default sort order, which you can change to descending. • Rows with equal values for the ranking criteria receive a different number. ROW_NUMBER ( ) OVER ( [query_partition_clause] order_by_clause )

58 ROW_NUMBER VS. ROWNUM • ROWNUM is a pseudo column, ROW_NUMBER is an actual function • ROWNUM requires sorting of the entire dataset in order to return ordered list • ROW_NUMBER will only sort the required rows thus giving better performance

59 Working With The NTILE Function • Not really a rank function • Divides an ordered data set into a number of buckets indicated by expr and assigns the appropriate bucket number to each row • The buckets are numbered 1 through expr NTILE ( expr ) OVER ([query_partition_clause] order_by_clause)

60 Summary of Ranking Functions • Different ranking functions may return different results if the data has ties SELECT last_name, salary, department_id, ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) A, RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) B, DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) C, PERCENT_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) D, NTILE(4) OVER (PARTITION BY department_id ORDER BY salary DESC) E FROM hr.employees; 60

62 Using the LAG and LEAD Analytic Functions • LAG provides access to more than one row of a table at the same time without a self-join. • Given a series of rows returned from a query and a position of the cursor, LAG provides access to a row at a given physical offset before that position. • If you do not specify the offset, its default is 1. • If the offset goes beyond the scope of the window, the optional default value is returned. If you do not specify the default, its value is NULL. {LAG | LEAD}(value_expr [, offset ] [, default ]) OVER ([ query_partition_clause ] order_by_clause)

63 Using the LAG and LEAD: Example SELECT time_id, TO_CHAR(SUM(amount_sold),'9,999,999') AS SALES, TO_CHAR(LAG(SUM(amount_sold),1) OVER (ORDER BY time_id),'9,999,999') AS LAG1, TO_CHAR(LEAD(SUM(amount_sold),1) OVER (ORDER BY time_id),'9,999,999') AS LEAD1 FROM sales WHERE time_id >= TO_DATE('10-OCT-2000') AND time_id <= TO_DATE('14-OCT-2000') GROUP BY time_id;

64 Using the LISTAGG Function • For a specified measure, LISTAGG orders data within each group specified in the ORDER BY clause and then concatenates the values of the measure column LISTAGG(measure_expr [, 'delimiter']) WITHIN GROUP (order_by_clause) [OVER query_partition_clause]

65 Using LISTAGG: Example SELECT department_id "Dept", hire_date "Date", last_name "Name", LISTAGG(last_name, ', ') WITHIN GROUP (ORDER BY hire_date, last_name) OVER (PARTITION BY department_id) as "Emp_list" FROM hr.employees WHERE hire_date < '01-SEP-2003' ORDER BY "Dept", "Date", "Name";

66 LISTAGG in Oracle 12c • Limited to output of 4000 chars or 32000 with extended column sizes • Oracle 12cR2 provides overflow handling: • Example: listagg ( measure_expr, ',' [ on overflow (truncate|error) ] [ text ] [ (with|without) count ] ) within group (order by cols) select listagg(table_name, ',' on overflow truncate) within group (order by table_name) table_names from dba_tables

67 Using the FIRST and LAST Functions • Both are aggregate and analytic functions • Used to retrieve a value from the first or last row of a sorted group, but the needed value is not the sort key • FIRST and LAST functions eliminate the need for self- joins or views and enable better performance aggregate_function KEEP (DENSE_RANK FIRST ORDER BY expr [ DESC | ASC ][ NULLS { FIRST | LAST } ] [, expr [ DESC | ASC ] [ NULLS { FIRST | LAST } ] ]... ) [ OVER query_partition_clause ]

68 FIRST and LAST Aggregate Example SELECT department_id, MIN(salary) KEEP (DENSE_RANK FIRST ORDER BY commission_pct) "Worst", MAX(salary) KEEP (DENSE_RANK LAST ORDER BY commission_pct) "Best" FROM employees GROUP BY department_id ORDER BY department_id;

69 FIRST and LAST Analytic Example SELECT last_name, department_id, salary, MIN(salary) KEEP (DENSE_RANK FIRST ORDER BY commission_pct) OVER (PARTITION BY department_id) "Worst", MAX(salary) KEEP (DENSE_RANK LAST ORDER BY commission_pct) OVER (PARTITION BY department_id) "Best" FROM employees ORDER BY department_id, salary, last_name;

70 Using FIRST_VALUE Analytic Function • Returns the first value in an ordered set of values • If the first value in the set is null, then the function returns NULL unless you specify IGNORE NULLS. This setting is useful for data densification. FIRST_VALUE (expr [ IGNORE NULLS ]) OVER (analytic_clause)

71 Using FIRST_VALUE: Example SELECT department_id, last_name, salary, FIRST_VALUE(last_name) OVER (ORDER BY salary ASC ROWS UNBOUNDED PRECEDING) AS lowest_sal FROM (SELECT * FROM employees WHERE department_id = 30 ORDER BY employee_id) ORDER BY department_id, last_name, salary, lowest_sal;

72 Using LAST_VALUE Analytic Function • Returns the last value in an ordered set of values. LAST_VALUE (expr [ IGNORE NULLS ]) OVER (analytic_clause)

73 Using NTH_VALUE Analytic Function • Returns the N-th values in an ordered set of values • Different default window: RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW NTH_VALUE (measure_expr, n) [ FROM { FIRST | LAST } ][ { RESPECT | IGNORE } NULLS ] OVER (analytic_clause)

74 Using NTH_VALUE: Example SELECT prod_id, channel_id, MIN(amount_sold), NTH_VALUE ( MIN(amount_sold), 2) OVER (PARTITION BY prod_id ORDER BY channel_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) nv FROM sh.sales WHERE prod_id BETWEEN 13 and 16 GROUP BY prod_id, channel_id;

75 Using NTH_VALUE: Example SELECT prod_id, channel_id, MIN(amount_sold), NTH_VALUE ( MIN(amount_sold), 2) OVER (PARTITION BY prod_id ORDER BY channel_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) nv FROM sh.sales WHERE prod_id BETWEEN 13 and 16 GROUP BY prod_id, channel_id;

77 Window Functions • The windowing_clause gives some analytic functions a further degree of control over this window within the current partition • The windowing_clause can only be used if an order_by_clause is present

78 Windows Can Be By RANGE Or ROWS Possible values for start_point and end_point UNBOUNDED PRECEDING The window starts at the first row of the partition. Only available for start points. UNBOUNDED FOLLOWING The window ends at the last row of the partition. Only available for end points. CURRENT ROW The window starts or ends at the current row value_expr PRECEDING A physical or logical offset before the current row. When used with RANGE, can also be an interval literal value_expr FOLLOWING As above, but an offset after the current row RANGE BETWEEN start_point AND end_point ROWS BETWEEN start_point AND end_point

79 Shortcuts • Useful shortcuts for the windowing clause: • The windows are limited to the current partition • Generally, the default window is the entire work set unless said otherwise ROWS UNBOUNDED PRECEDING ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ROWS 10 PRECEDING ROWS BETWEEN 10 PRECEDING AND CURRENT ROW ROWS CURRENT ROW ROWS BETWEEN CURRENT ROW AND CURRENT ROW

80 Windowing Clause Useful Usages • Cumulative aggregation • Sliding average over proceeding and/or following rows • Using the RANGE parameter to filter aggregation records

Pivot and Unpivot Turning things around!

82 PIVOT and UNPIVOT • You can use the PIVOT operator of the SELECT statement to write cross-tabulation queries that rotate the column values into new columns, aggregating data in the process. • You can use the UNPIVOT operator of the SELECT statement to rotate columns into values of a column. PIVOT UNPIVOT

83 Pivoting on the QUARTER Column: Conceptual Example 30,000 40,000 60,000 30,000 40,000 20,000 AMOUNT_ SOLD 2,500Q1IUSAKids Jeans 2,000Q2CJapanKids Jeans 2,000Q3SUSAShorts I P C CHANNEL Kids Jeans Shorts Shorts PRODUCT 1,000Q2Germany 1,500Q4USA Q2 QUARTER 2,500Poland QUANTITY_ SOLD COUNTRY 2,000 Q3 Kids Jeans Shorts PRODUCT 3,500 2,000 Q2 1,5002,500 Q4Q1

84 Pivoting Before Oracle 11g • Pivoting the data before 11g was a complex query which required the use of the CASE or DECODE functions select product, sum(case when quarter = 'Q1' then amount_sold else null end) Q1, sum(case when quarter = 'Q2' then amount_sold else null end) Q2, sum(case when quarter = 'Q3' then amount_sold else null end) Q3, sum(case when quarter = 'Q4' then amount_sold else null end) Q4 from sales group by product;

85 PIVOT Clause Syntax table_reference PIVOT [ XML ] ( aggregate_function ( expr ) [[AS] alias ] [, aggregate_function ( expr ) [[AS] alias ] ]... pivot_for_clause pivot_in_clause ) -- Specify the column(s) to pivot whose values are to -- be pivoted into columns. pivot_for_clause = FOR { column |( column [, column]... ) } -- Specify the pivot column values from the columns you -- specified in the pivot_for_clause. pivot_in_clause = IN ( { { { expr | ( expr [, expr]... ) } [ [ AS] alias] }... | subquery | { ANY | ANY [, ANY]...} } )

87 Creating a New View: Example CREATE OR REPLACE VIEW sales_view AS SELECT prod_name AS product, country_name AS country, channel_id AS channel, SUBSTR(calendar_quarter_desc, 6,2) AS quarter, SUM(amount_sold) AS amount_sold, SUM(quantity_sold) AS quantity_sold FROM sales, times, customers, countries, products WHERE sales.time_id = times.time_id AND sales.prod_id = products.prod_id AND sales.cust_id = customers.cust_id AND customers.country_id = countries.country_id GROUP BY prod_name, country_name, channel_id, SUBSTR(calendar_quarter_desc, 6, 2);

89 Selecting the SALES VIEW Data SELECT product, country, channel, quarter, quantity_sold FROM sales_view; PRODUCT COUNTRY CHANNEL QUARTER QUANTITY_SOLD ------------ ------------ ---------- -------- ------------- Y Box Italy 4 01 21 Y Box Italy 4 02 17 Y Box Italy 4 03 20 . . . Y Box Japan 2 01 35 Y Box Japan 2 02 39 Y Box Japan 2 03 36 Y Box Japan 2 04 46 Y Box Japan 3 01 65 . . . Bounce Italy 2 01 34 Bounce Italy 2 02 43 . . . 9502 rows selected.

90 Pivoting the QUARTER Column in the SH Schema: Example SELECT * FROM (SELECT product, quarter, quantity_sold FROM sales_view) PIVOT (sum(quantity_sold) FOR quarter IN ('01', '02', '03', '04')) ORDER BY product DESC; . . .

92 Unpivoting the QUARTER Column: Conceptual Example 2,000 Q3 Kids Jeans Shorts PRODUCT 3,500 2,000 Q2 1,5002,500 Q4Q1 2,500Q1Kids Jeans 2,000Q2Kids Jeans 3,500Q2Shorts 1,500Q4Kids Jeans Q3 QUARTER 2,000Shorts SUM_OF_QUANTITYPRODUCT

93 Unpivoting Before Oracle 11g • Univoting the data before 11g requires multiple queries on the table using the UNION ALL operator SELECT * FROM ( SELECT product, '01' AS quarter, Q1_value FROM sales UNION ALL SELECT product, '02' AS quarter, Q2_value FROM sales UNION ALL SELECT product, '03' AS quarter, Q3_value FROM sales UNION ALL SELECT product, '04' AS quarter, Q4_value FROM sales );

94 Using the UNPIVOT Operator • An UNPIVOT operation does not reverse a PIVOT operation; instead, it rotates data found in multiple columns of a single row into multiple rows of a single column. • If you are working with pivoted data, UNPIVOT cannot reverse any aggregations that have been made by PIVOT or any other means. UNPIVOT

95 Using the UNPIVOT Clause • The UNPIVOT clause rotates columns from a previously pivoted table or a regular table into rows. You specify: – The measure column or columns to be unpivoted – The name or names for the columns that result from the UNPIVOT operation – The columns that are unpivoted back into values of the column specified in pivot_for_clause • You can use an alias to map the column name to another value.

96 UNPIVOT Clause Syntax table_reference UNPIVOT [{INCLUDE|EXCLUDE} NULLS] -- specify the measure column(s) to be unpivoted. ( { column | ( column [, column]... ) } unpivot_for_clause unpivot_in_clause ) -- Specify one or more names for the columns that will -- result from the unpivot operation. unpivot_for_clause = FOR { column | ( column [, column]... ) } -- Specify the columns that will be unpivoted into values of -- the column specified in the unpivot_for_clause. unpivot_in_clause = ( { column | ( column [, column]... ) } [ AS { constant | ( constant [, constant]... ) } ] [, { column | ( column [, column]... ) } [ AS { constant | ( constant [, constant]...) } ] ]...)

97 Creating a New Pivot Table: Example . . . CREATE TABLE pivotedtable AS SELECT * FROM (SELECT product, quarter, quantity_sold FROM sales_view) PIVOT (sum(quantity_sold) FOR quarter IN ('01' AS Q1, '02' AS Q2, '03' AS Q3, '04' AS Q4)); SELECT * FROM pivotedtable ORDER BY product DESC;

98 Unpivoting the QUARTER Column : Example • Unpivoting the QUARTER Column in the SH Schema: SELECT * FROM pivotedtable UNPIVOT (quantity_sold For Quarter IN (Q1, Q2, Q3, Q4)) ORDER BY product DESC, quarter; . . .

99 More Examples… • More information and examples could be found on my Blog: http://www.realdbamagic.com/he/pivot-a-table/

Top-N and Paging Queries In Oracle 12c

101 Top-N Queries • A Top-N query is used to retrieve the top or bottom N rows from an ordered set • Combining two Top-N queries gives you the ability to page through an ordered set • Oracle 12c has introduced the row limiting clause to simplify Top-N queries

102 Top-N in 12cR1 • This is ANSI syntax • The default offset is 0 • Null values in offset, rowcount or percent will return no rows [ OFFSET offset { ROW | ROWS } ] [ FETCH { FIRST | NEXT } [ { rowcount | percent PERCENT } ] { ROW | ROWS } { ONLY | WITH TIES } ]

103 Top-N Examples SELECT last_name, salary FROM hr.employees ORDER BY salary FETCH FIRST 4 ROWS ONLY; SELECT last_name, salary FROM hr.employees ORDER BY salary FETCH FIRST 4 ROWS WITH TIES; SELECT last_name, salary FROM hr.employees ORDER BY salary DESC FETCH FIRST 10 PERCENT ROWS ONLY;

104 Paging Before 12c • Before 12c we had to use the rownum pseudo column to filter out rows • That will require sorting the entire rowset SELECT val FROM (SELECT val, rownum AS rnum FROM (SELECT val FROM rownum_order_test ORDER BY val) WHERE rownum <= 10) WHERE rnum >= 5;

105 Paging in Oracle 12c • After 12c we have a syntax improvement for paging using the Top-N queries • This will use ROW_NUMBER and RANK in the background – there is no real optimization improvements SELECT val FROM rownum_order_test ORDER BY val OFFSET 4 ROWS FETCH NEXT 5 ROWS ONLY;

106 More Examples • More information and examples could be found on my blog: http://www.realdbamagic.com/he/12c-top-n-query/

107 Analytic Functions and Performance • Analytic functions has positive impact on performance for the most part • Using analytic functions can reduce the number of table scans and reduce IO consumption • The query might use more CPU and/or memory but it will usually run faster than the same result without analytic functions • Top-N queries might struggle with cardinality evaluation when using the “With Ties” option

Common Table Expression and Subquery Factoring

109 Subquery Factoring • The WITH clause, or subquery factoring clause, is part of the SQL-99 standard • Introduced in Oracle 9.2 • The WITH produces a new inline view which we can query from • Sometimes, the subquery is being cached (materialized) so it does not need to re-query the data again

110 Subquery Example SELECT e.LAST_NAME AS employee_name, dc.dept_count AS emp_dept_count FROM employees e, (SELECT DEPARTMENT_ID, COUNT(*) AS dept_count FROM employees GROUP BY DEPARTMENT_ID) dc WHERE e.DEPARTMENT_ID = dc.DEPARTMENT_ID; WITH dept_count AS ( SELECT DEPARTMENT_ID, COUNT(*) AS dept_count FROM employees GROUP BY DEPARTMENT_ID) SELECT e.LAST_NAME AS employee_name, dc.dept_count AS emp_dept_count FROM employees e, dept_count dc WHERE e.DEPARTMENT_ID = dc.DEPARTMENT_ID;

111 Subquery Reuse WITH dept_count AS ( SELECT DEPARTMENT_ID, COUNT(*) AS dept_count FROM employees GROUP BY DEPARTMENT_ID) SELECT e1.LAST_NAME AS employee_name, e2.LAST_NAME as Manager_name, dc1.dept_count AS emp_dept_count, dc2.dept_count as mgr_dept_count FROM employees e1, employees e2, dept_count dc1, dept_count dc2 WHERE e1.DEPARTMENT_ID = dc1.DEPARTMENT_ID and e2.DEPARTMENT_ID = dc2.DEPARTMENT_ID and e1.MANAGER_ID = e2.employee_id

112 Functions in the WITH Clause (12.1) • Oracle 12c allows us the definition of anonymous function within the scope of a query with function sumascii (str in varchar2) return number is x number := 0; begin for i in 1..length (str) loop x := x + ascii (substr (str, i, 1)) ; end loop; return x; end; select /*+ WITH_PLSQL */ h.EMPLOYEE_ID, h.last_name, sumascii (h.last_name) from hr.employees h

Hierarchical Queries and Recursive Queries

114 Using Hierarchical Queries • You can use hierarchical queries to retrieve data based on a natural hierarchical relationship between rows in a table. • A relational database does not store records in a hierarchical way; therefore, a hierarchical query is possible only when a relationship exists between rows in a table. • However, where a hierarchical relationship exists between the rows of a single table, a process called “tree walking” enables the hierarchy to be constructed. • A hierarchical query is a method of reporting, with the branches of a tree in a specific order.

115 Business Challenges • Getting all employees that report directly or indirectly to a manager • Managing documents and folders • Managing privileges • Aggregating levels on the same row

116 Using Hierarchical Queries: Example • Sample Data from the EMPLOYEES Table (HR schema) • Kochhar, De Haan, and Hartstein report to the same manager (MANAGER_ID = 100) • EMPLOYEE_ID = 100 is King …

117 Natural Tree Structure De Haan HunoldWhalen Kochhar Higgins Mourgos Zlotkey Rajs Davies Matos Gietz Ernst Lorentz Hartstein Fay Abel Taylor Grant Vargas MANAGER_ID = 100 (Child) EMPLOYEE_ID = 100 (Parent) . . . . . . . . . . . . . . . King

118 Hierarchical Queries: Syntax • condition: expr comparison_operator expr SELECT [LEVEL], column, expr... FROM table [WHERE condition(s)] [START WITH condition(s)] [CONNECT BY PRIOR condition(s)] ;

119 Walking the Tree: Specifying the Starting Point • Use the START WITH clause to specify the starting point, that is, the row or rows to be used as the root of the tree: – Specifies the condition that must be met – Accepts any condition that is valid in a WHERE clause • For example, using the HR.EMPLOYEES table, start with the employee whose last name is Kochhar. . . . START WITH last_name = 'Kochhar' START WITH column1 = value

120 Walking the Tree: Specifying the Direction • The direction of the query is determined by the CONNECT BY PRIOR column placement. • The PRIOR operator refers to the parent row. CONNECT BY PRIOR column1 = column2 . . . CONNECT BY PRIOR employee_id = manager_id . . . Parent key Child key

121 Hierarchical Query Example: Using the CONNECT BY Clause SELECT employee_id, last_name, manager_id FROM hr.employees; . . .

122 Specifying the Direction of the Query: From the Top Down SELECT last_name||' reports to '|| PRIOR last_name "Walk Top Down" FROM hr.employees START WITH last_name = 'King' CONNECT BY PRIOR employee_id = manager_id ; . . .

123 Specifying the Direction of the Query: From the Bottom Up SELECT employee_id, last_name, job_id, manager_id FROM hr.employees START WITH employee_id = 101 CONNECT BY PRIOR manager_id = employee_id ;

124 Using the LEVEL Pseudocolumn Level 1 root/ parent Level 3 parent/ child/leaf Level 4 leaf De Haan King HunoldWhalen Kochhar Higgins Mourgos Zlotkey Rajs Davies Matos Gietz Ernst Lorentz Hartstein Fay Abel Taylor Grant Vargas Level 2 parent/ child

125 Using the LEVEL Pseudocolumn: Example SELECT employee_id, last_name, manager_id, LEVEL FROM hr.employees START WITH employee_id = 100 CONNECT BY PRIOR employee_id = manager_id ORDER siblings BY last_name; . . .

126 Formatting Hierarchical Reports • It is common to format Hierarchical reports using LEVEL and LPAD – Create a report displaying company management levels beginning with the highest level and indenting each of the following levels. SELECT LPAD(last_name, LENGTH(last_name)+ (LEVEL*2)-2,'_') AS org_chart FROM hr.employees START WITH first_name = 'Steven' AND last_name = 'King' CONNECT BY PRIOR employee_id = manager_id ORDER SIBLINGS BY last_name;

128 Pruning Nodes and Branches • Use the WHERE clause to eliminate a node • Use the CONNECT BY clause to eliminate a branch Kochhar Higgins Gietz Whalen Kochhar HigginsWhalen Gietz . . . WHERE last_name != 'Higgins' . . . CONNECT BY PRIOR employee_id = manager_id AND last_name != 'Higgins' 1 2

129 Pruning Branches Example 1: Eliminating a Node SELECT department_id, employee_id,last_name, job_id, salary FROM hr.employees WHERE last_name != 'Higgins' START WITH manager_id IS NULL CONNECT BY PRIOR employee_id = manager_id; . . . . . . . . .

130 Pruning Branches Example 2: Eliminating a Branch SELECT department_id, employee_id,last_name, job_id, salary FROM hr.employees START WITH manager_id IS NULL CONNECT BY PRIOR employee_id = manager_id AND last_name != 'Higgins'; . . .

131 Order of Precedence • Join happens before connect by • Where is happening after connect by • Regular order by will rearrange the returning rows • Sibling order by will rearrange the returning rows for each level

132 Other Connect By Functions • CONNECT_BY_ISCYCLE • CONNECT_BY_ISLEAF • CONNECT_BY_ROOT • SYS_CONNECT_BY_PATH

133 Recursive Subquery Factoring • ANSI SQL:2008 (Oracle 11g) introduced a new way to run hierarchical queries: Recursive Subquery Factoring using Subquery Factoring • That will mean that a query will query itself using the WITH clause, making queries easier to write

137 Recursive Subquery Factoring Example with mytree(id, parent_id, "level") as ( select id, parent_id, 1 as "level" from temp_v where id = 1 union all select temp_v.id, temp_v.parent_id, mytree."level" + 1 from temp_v, mytree where temp_v.parent_id = mytree.id ) Select * from mytree; Stop Condition Actual Recursion

139 Warning: Performance • Recursion and Hierarchies might have bad impact on performance • Watch out for mega-trees – it has CPU and memory impacts • Using recursion might lead for multiple IO reads of the same blocks

141 Regular Expression • Regular expression (regexp) is a sequence of characters that define a search pattern • Commonly used for smart “Search and Replace” of patterns and for input validations of text • Widely introduced in Oracle 10g (and it even existed even before that)

142 Common REGEXP Functions and Operators REGEXP_LIKE Perform regular expression matching REGEXP_REPLACE Extends the functionality of the REPLACE function by using patterns REGEXP_SUBSTR Extends the functionality of the SUBSTR function by using patterns REGEXP_COUNT Count the number of matches of the pattern in a given string REGEXP_INSTR Extends the functionality of the INSTR function by using patterns

143 Supported Regular Expression Patterns • Concatenation: No operator between elements. • Quantifiers: – . Matches any character in the database character set – * 0 or more matches – + 1 or more matches – ? 0 or 1 match – {n} Exactly n matches – {n,} n or more matches – {n, m} Between n and m (inclusive) matches – {, m} Between 0 an m (inclusive) matches • Alternation: [|] • Grouping: ()

144 Supported Regular Expression Patterns Value Description ^ Matches the beginning of a string. If used with a match_parameter of 'm', it matches the start of a line anywhere within expression. $ Matches the end of a string. If used with a match_parameter of 'm', it matches the end of a line anywhere withinexpression. W Matches a nonword character. s Matches a whitespace character. S matches a non-whitespace character. A Matches the beginning of a string or matches at the end of a string before a newline character. Z Matches at the end of a string.

145 Character Classes Character Class Description [:alnum:] Alphanumeric characters [:alpha:] Alphabetic characters [:blank:] Blank Space Characters [:cntrl:] Control characters (nonprinting) [:digit:] Numeric digits [:graph:] Any [:punct:], [:upper:], [:lower:], and [:digit:] chars [:lower:] Lowercase alphabetic characters [:print:] Printable characters [:punct:] Punctuation characters [:space:] Space characters (nonprinting), such as carriage return, newline, vertical tab, and form feed [:upper:] Uppercase alphabetic characters [:xdigit:] Hexidecimal characters

147 Pitfalls • Regular expressions might be slow when used on large amount of data • Writing regular expression can be very tricky – make sure your pattern is correct • Oracle REGEXP syntax is not standard, regular expression might not work or partially work causing wrong results • There can only be up to 9 placeholders in a given quantifier

Pattern Matching in Oracle 12c

149 What is Pattern Matching • Identify and group rows with consecutive values • Consecutive in this regards – row after row • Uses regular expression like syntax to find patterns

150 Common Business Challenges • Finding sequences of events in security applications • Locating dropped calls in a CDR listing • Financial price behaviors (V-shape, W-shape U- shape, etc.) • Fraud detection and sensor data analysis

151 MATCH_RECOGNIZE Syntax SELECT FROM [row pattern input table] MATCH_RECOGNIZE` ( [ PARTITION BY <cols> ] [ ORDER BY <cols> ] [ MEASURES <cols> ] [ ONE ROW PER MATCH | ALL ROWS PER MATCH ] [ SKIP_TO_option] PATTERN ( <row pattern> ) DEFINE <definition list> )

152 Basix Syntax Legend • PARTITION BY divides the data in to logical groups • ORDER BY orders the data in each logical group • MEASURES define the data measures of the pattern • ONE/ALL ROW PER MATCH defines what to do with the pattern – return one row or all rows • PATTERN says what the pattern actually is • DEFINE gives us the condition that must be met for a row to map to the pattern variables

153 MATCH_RECOGNIZE Example • Find Simple V-Shape with 1 row output per match SELECT * FROM Ticker MATCH_RECOGNIZE ( PARTITION BY symbol ORDER BY tstamp MEASURES STRT.tstamp AS start_tstamp, LAST(DOWN.tstamp) AS bottom_tstamp, LAST(UP.tstamp) AS end_tstamp ONE ROW PER MATCH AFTER MATCH SKIP TO LAST UP PATTERN (STRT DOWN+ UP+) DEFINE DOWN AS DOWN.price < PREV(DOWN.price), UP AS UP.price > PREV(UP.price) ) MR ORDER BY MR.symbol, MR.start_tstamp;

155 Example: Sequential Employee IDs • Our goal: find groups of users with sequences IDs • This can be useful for detecting missing employees in a table, or to locate “gaps” in a group FIRSTEMP LASTEMP ---------- ---------- 7371 7498 7500 7520 7522 7565 7567 7653 7655 7697 7699 7781 7783 7787 7789 7838

156 Pattern Matching Example SELECT * FROM Emps MATCH_RECOGNIZE ( ORDER BY emp_id PATTERN (STRT B*) DEFINE B AS emp_id = PREV(emp_id)+1 ONE ROW PER MATCH MEASURES STRT.emp_id firstemp, LAST(emp_id) lastemp AFTER MATCH SKIP PAST LAST ROW ); 1. Define input 2. Pattern Matching 3. Order input 4. Process pattern 5. Using defined conditions 6. Output: rows per match 7. Output: columns per row 8. Where to go after match? Original concept by Stew Ashton

157 Pattern Matching Example (Actual Syntax) SELECT * FROM Emps MATCH_RECOGNIZE ( ORDER BY emp_id MEASURES STRT.emp_id firstemp, LAST(emp_id) lastemp ONE ROW PER MATCH AFTER MATCH SKIP PAST LAST ROW PATTERN (STRT B*) DEFINE B AS emp_id = PREV(emp_id)+1 ); 1. Define input 2. Pattern Matching 3. Order input 4. Process pattern 5. Using defined conditions 6. Output: rows per match 7. Output: columns per row 8. Where to go after match?

158 Oracle 11g Analytic Function Solution select firstemp, lastemp From (select nvl (lag (r) over (order by r), minr) firstemp, q lastemp from (select emp_id r, lag (emp_id) over (order by emp_id) q, min (emp_id) over () minr, max (emp_id) over () maxr from emps e1) where r != q + 1 -- groups including lower end union select q, nvl (lead (r) over (order by r), maxr) from ( select emp_id r, lead (emp_id) over (order by emp_id) q, min (emp_id) over () minr, max (emp_id) over () maxr from emps e1) where r + 1 != q -- groups including higher end );

159 Supported Regular Expression Patterns • Concatenation: No operator between elements. • Quantifiers: – * 0 or more matches. – + 1 or more matches – ? 0 or 1 match. – {n} Exactly n matches. – {n,} n or more matches. – {n, m} Between n and m (inclusive) matches. – {, m} Between 0 an m (inclusive) matches. • Alternation: | • Grouping: ()

160 Functions • CLASSIFIER(): Which pattern variable applies to which row • MATCH_NUMBER(): Which rows are members of which match • PREV(): Access to a column/expression in a previous row • NEXT(): Access to a column/expression in the next row • LAST(): Last value within the pattern match • FIRST(): First value within the pattern match • COUNT(), AVG(), MAX(), MIN(), SUM()

161 Example: All Rows Per Match • Find suspicious transfers – a large transfer after 3 small ones SELECT userid, match_id, pattern_variable, time, amount FROM (SELECT * FROM event_log WHERE event = 'transfer') MATCH_RECOGNIZE ( PARTITION BY userid ORDER BY time MEASURES MATCH_NUMBER() match_id, CLASSIFIER() pattern_variable ALL ROWS PER MATCH PATTERN ( x{3,} y) DEFINE x AS (amount < 2000 AND LAST(x.time) -FIRST(x.time) < 30), y AS (amount >= 1000000 AND y.time-LAST(x.time) < 10) );

162 The Output • MATCH_ID shows current match sequence • PATTERN_VARIABLE show which variable was applied • USERID is the partition key USERID MATCH_ID PATTERN_VA TIME AMOUNT -------- ---------- ---------- --------- ---------- john 1 X 06-JAN-12 1000 john 1 X 15-JAN-12 1500 john 1 X 20-JAN-12 1500 john 1 X 23-JAN-12 1000 john 1 Y 26-JAN-12 1000000

163 Example: One Row Per Match • Same as before – show one row per match SELECT userid, first_trx, last_trx, amount FROM (SELECT * FROM event_log WHERE event = 'transfer') MATCH_RECOGNIZE ( PARTITION BY userid ORDER BY time MEASURES FIRST(x.time) first_trx, y.time last_trx, y.amount amount ONE ROW PER MATCH PATTERN ( x{3,} y ) DEFINE x AS (amount < 2000 AND LAST(x.time) -FIRST(x.time) < 30), y AS (amount >= 1000000 AND y.time-LAST(x.time) < 10) );

164 The Output • USERID is the partition key • FIRST_TRX is a calculated measure • AMOUNT and LAST_TRX are measures USERID FIRST_TRX LAST_TRX AMOUNT -------- --------- --------- ---------- john 06-JAN-12 26-JAN-12 1000000

165 Few Last Tips • Test all cases: pattern matching can be very tricky • Don’t forget to test your data with no matches • There is no LISTAGG and no DISTINCT when using match recognition • Pattern variables cannot be used as bind variables

167 What is XML • XML stand for eXtensible Markup Language • Defines a set of rules for encoding documents in a format which is both human-readable and machine-readable • Data is unstructured and can be transferred easily to other system

168 XML Terminology • Root • Element • Attribute • Forest • XML Fragment • XML Document

169 What Does XML Look Like? <?xml version="1.0"?> <ROWSET> <ROW> <USERNAME>SYS</USERNAME> <USER_ID>0</USER_ID> <CREATED>28-JAN-08</CREATED> </ROW> <ROW> <USERNAME>SYSTEM</USERNAME> <USER_ID>5</USER_ID> <CREATED>28-JAN-08</CREATED> </ROW> </ROWSET>

170 Generating XML From Oracle • Concatenating strings – building the XML manually. This is highly not recommended • Using DBMS_XMLGEN • Using ANSI SQL:2003 XML functions

171 Using DBMS_XMLGEN • The DBMS_XMLGEN package converts the results of a SQL query to a canonical XML format • The package takes an arbitrary SQL query as input, converts it to XML format, and returns the result as a CLOB • Using the DBMS_XMLGEN we can create contexts and use it to build XML documents • Old package – exists since Oracle 9i

172 Example of Using DBMS_XMLGEN select dbms_xmlgen.getxml(q'{ select column_name, data_type from all_tab_columns where table_name = 'EMPLOYEES' and owner = 'HR'}') from dual / <?xml version="1.0"?> <ROWSET> <ROW> <COLUMN_NAME>EMPLOYEE_ID</COLUMN_NAME> <DATA_TYPE>NUMBER</DATA_TYPE> </ROW> <ROW> <COLUMN_NAME>FIRST_NAME</COLUMN_NAME> <DATA_TYPE>VARCHAR2</DATA_TYPE> </ROW> [...] </ROWSET>

173 Why Not Use DBMS_XMLGEN • DBMS_XMLGEN is an old package (9.0 and 9i) • Any context change requires complex PL/SQL • There are improved ways to use XML in queries • Use DBMS_XMLGEN for the “quick and dirty” solution only

174 Standard XML Functions • Introduced in ANSI SQL:2003 – Oracle 9iR2 and 10gR2 • Standard functions that can be integrated into queries • Removes the need for PL/SQL code to create XML documents

175 XML Functions XMLELEMENT The basic unit for turning column data into XML fragments XMLATTRIBUTES Converts column data into attributes of the parent element XMLFOREST Allows us to process multiple columns at once XMLAGG Aggregate separate Fragments into a single fragment XMLROOT Allows us to place an XML tag at the start of our XML document

176 XMLELEMENT SELECT XMLELEMENT("name", e.last_name) AS employee FROM employees e WHERE e.employee_id = 202; EMPLOYEE ------------------------------ <name>Fay</name>

177 XMLELEMENT (2) SELECT XMLELEMENT("employee", XMLELEMENT("works_number", e.employee_id), XMLELEMENT("name", e.last_name) ) AS employee FROM employees e WHERE e.employee_id = 202; EMPLOYEE ---------------------------------------------------------- <employee><works_number>202</works_number><name>Fay</name> </employee>

178 XMLATTRIBUTES SELECT XMLELEMENT("employee", XMLATTRIBUTES( e.employee_id AS "works_number", e.last_name AS "name") ) AS employee FROM employees e WHERE e.employee_id = 202; EMPLOYEE ---------------------------------------------------------- <employee works_number="202" name="Fay"></employee>

179 XMLFOREST SELECT XMLELEMENT("employee", XMLFOREST( e.employee_id AS "works_number", e.last_name AS "name", e.phone_number AS "phone_number") ) AS employee FROM employees e WHERE e.employee_id = 202; EMPLOYEE ---------------------------------------------------------- <employee><works_number>202</works_number><name>Fay</name> <phone_number>603.123.6666</phone_number></employee>

180 XMLFOREST Problem SELECT XMLELEMENT("employee", XMLFOREST( e.employee_id AS "works_number", e.last_name AS "name", e.phone_number AS "phone_number") ) AS employee FROM employees e WHERE e.employee_id in (202, 203); EMPLOYEE ---------------------------------------------------------- <employee><works_number>202</works_number><name>Fay</name> <phone_number>603.123.6666</phone_number></employee> <employee><works_number>203</works_number><name>Mavris</name> <phone_number>515.123.7777</phone_number></employee> 2 row selected.

181 XMLAGG SELECT XMLAGG( XMLELEMENT("employee", XMLFOREST( e.employee_id AS "works_number", e.last_name AS "name", e.phone_number AS "phone_number") )) AS employee FROM employees e WHERE e.employee_id in (202, 203); EMPLOYEE ---------------------------------------------------------- <employee><works_number>202</works_number><name>Fay</name> <phone_number>603.123.6666</phone_number></employee><employee> <works_number>203</works_number><name>Mavris</name> <phone_number>515.123.7777</phone_number></employee> 1 row selected.

182 XMLROOT • Creating a well formed XML document SELECT XMLROOT ( XMLELEMENT("employees", XMLAGG( XMLELEMENT("employee", XMLFOREST( e.employee_id AS "works_number", e.last_name AS "name", e.phone_number AS "phone_number") ))), VERSION '1.0') AS employee FROM employees e WHERE e.employee_id in (202, 203);

183 XMLROOT • Well formed, version bound, beatified XML: EMPLOYEE ------------------------------------------ <?xml version="1.0"?> <employees> <employee> <works_number>202</works_number> <name>Fay</name> <phone_number>603.123.6666</phone_number> </employee> <employee> <works_number>203</works_number> <name>Mavris</name> <phone_number>515.123.7777</phone_number> </employee> </employees>

184 Using XQuery • Using the XQuery language we can create, read and manipulate XML documents • Two main functions: XMLQuery and XMLTable • XQuery is about sequences - XQuery is a general sequence-manipulation language • Each sequence can contain numbers, strings, Booleans, dates, or other XML fragments

185 Creating XML Document using XQuery SELECT warehouse_name, EXTRACTVALUE(warehouse_spec, '/Warehouse/Area'), XMLQuery( 'for $i in /Warehouse where $i/Area > 50000 return <Details> <Docks num="{$i/Docks}"/> <Rail> { if ($i/RailAccess = "Y") then "true" else "false" } </Rail> </Details>' PASSING warehouse_spec RETURNING CONTENT) "Big_warehouses" FROM warehouses;

186 Creating XML Document using XQuery WAREHOUSE_ID Area Big_warehouses ------------ --------- -------------------------------------------------------- 1 25000 2 50000 3 85700 <Details><Docks></Docks><Rail>false</Rail></Details> 4 103000 <Details><Docks num="3"></Docks><Rail>true</Rail></Details> . . .

187 Example: Using XMLTable to Read XML SELECT lines.lineitem, lines.description, lines.partid, lines.unitprice, lines.quantity FROM purchaseorder, XMLTable('for $i in /PurchaseOrder/LineItems/LineItem where $i/@ItemNumber >= 8 and $i/Part/@UnitPrice > 50 and $i/Part/@Quantity > 2 return $i' PASSING OBJECT_VALUE COLUMNS lineitem NUMBER PATH '@ItemNumber', description VARCHAR2(30) PATH 'Description', partid NUMBER PATH 'Part/@Id', unitprice NUMBER PATH 'Part/@UnitPrice', quantity NUMBER PATH 'Part/@Quantity') lines;

189 What is JSON • JavaScript Object Notation • Converts database tables to a readable document – just like XML but simpler • Very common in NoSQL and Big Data solutions {"FirstName" : "Zohar", "LastName" : "Elkayam", "Age" : 36, "Connection" : [ {"Type" : “Email", "Value" : "zohar@DBAces.com"}, {"Type" : “Twitter", "Value" : “@realmgic"}, {"Type" : "Site", "Value" : "www.realdbamagic.com"}, ]}

190 JSON Benefits • Ability to store data without requiring a Schema – Store semi-structured data in its native (aggregated) form • Ability to query data without knowledge of Schema • Ability to index data with knowledge of Schema

191 Oracle JSON Support • Oracle supports JSON since version 12.1.0.2 • JSON documents stored in the database using existing data types: VARCHAR2, CLOB or BLOB • External JSON data sources accessible through external tables including HDFS • Data accessible via REST API

192 REST based API for JSON documents • Simple well understood model • CRUD operations are mapped to HTTP Verbs – Create / Update : PUT / POST – Retrieve : GET – Delete : DELETE – QBE, Bulk Update, Utilitiy functions : POST • Stateless

193 JSON Path Expression • Similar role to XPATH in XML • Syntactically similar to Java Script (. and [ ]) • Compatible with Java Script

194 Common JSON SQL Functions • There are few common JSON Operators: JSON_EXISTS Checks if a value exists in the JSON JSON_VALUE Retrieve a scalar value from JSON JSON_QUERY Query a string from JSON Document JSON_TABLE Query data from JSON Document (like XMLTable)

195 JSON_QUERY • Extract JSON fragment from JSON document select count(*) from J_PURCHASEORDER where JSON_EXISTS( PO_DOCUMENT, '$.ShippingInstructions.Address.state‘) /

196 Using JSON_TABLE • Generate rows from a JSON Array • Pivot properties / key values into columns • Use Nested Path clause to process multi-level collections with a single JSON_TABLE operator.

197 Example: JSON_TABLE • 1 Row of output for each row in table select M.* from J_PURCHASEORDER p, JSON_TABLE( p.PO_DOCUMENT, '$' columns PO_NUMBER NUMBER(10) path '$.PONumber', REFERENCE VARCHAR2(30 CHAR) path '$.Reference', REQUESTOR VARCHAR2(32 CHAR) path '$.Requestor', USERID VARCHAR2(10 CHAR) path '$.User', COSTCENTER VARCHAR2(16) path '$.CostCenter' ) M where PO_NUMBER > 1600 and PO_Number < 1605 /

198 Example: JSON_TABLE (2) • 1 row output for each member of LineItems array select D.* from J_PURCHASEORDER p, JSON_TABLE( p.PO_DOCUMENT, '$' columns( PO_NUMBER NUMBER(10) path '$.PONumber', NESTED PATH '$.LineItems[*]' columns( ITEMNO NUMBER(16) path '$.ItemNumber', UPCCODE VARCHAR2(14 CHAR) path '$.Part.UPCCode‘ )) ) D where PO_NUMBER = 1600 or PO_NUMBER = 1601 /

199 JSON Indexing • Known Query Patterns : JSON Path expression – Functional indexes using JSON_VALUE and, JSON_EXISTS – Materialized View using JSON_TABLE() • Ad-hoc Query Strategy – Based on Oracle’s full text index (Oracle Text) – Support ad-hoc path, value and keyword query search using JSON Path expressions

200 JSON in 12.2.0.1 • JSON in 12cR1 used to work with JSON documents stored in the database • 12cR2 brought the ability to create and modify JSON: – JSON_object – JSON_objectagg – JSON_array – JSON_arrayagg

201 JSON Creation Example select json_object ( 'department' value d.department_name, 'employees' value json_arrayagg ( json_object ( 'name' value first_name || ',' || last_name, 'job' value job_title ))) from hr.departments d, hr.employees e, hr.jobs j where d.department_id = e.department_id and e.job_id = j.job_id group by d.department_name;

Oracle 12c (12.1 and 12.2) New Features

203 Object Names Length (12.2) • Up to Oracle 12cR2, objects name length (tables, columns, indexes, constraints etc.) were limited to 30 chars • Starting Oracle 12cR2, length is now limited to 128 bytes create table with_a_really_really_really_really_really_long_name ( and_lots_and_lots_and_lots_and_lots_and_lots_of int, really_really_really_really_really_long_columns int );

204 Verify Data Type Conversions (12.2) • If we try to validate using regular conversion we might hit an error: ORA-01858: a non-numeric character was found where a numeric was expected • Use validate_conversion to validate the data without an error select t.* from dodgy_dates t where validate_conversion(is_this_a_date as date) = 1; select t.* from dodgy_dates t where validate_conversion(is_this_a_date as date, 'yyyymmdd') = 1;

205 Handle Casting Conversion Errors (12.2) • Let’s say we convert the value of a column using cast. What happens if some of the values doesn’t fit? • The cast function can now handle conversion errors: select cast ( 'not a date' as date default date'0001-01-01' on conversion error ) dt from dual;

206 Approximate Query (12.1) • APPROX_COUNT_DISTINCT returns the approximate number of rows that contain distinct values of expression • This gives better performance but might not return the exact result • Very good for large sets where exact values aren’t significant • Adjustable using ERROR_RATE and CONFIDENCE parametes

207 Approximate Query Performance

208 Approximate Query Enhancements (12.2) • 12.2 introduced a parameter, approx_for_count_distinct which automatically replace count distinct with APPROX_COUNT_DISTINCT • New approximate function: approx_percentile approx_percentile ( <expression> [ deterministic ], [ ('ERROR_RATE' | 'CONFIDENCE') ] ) within group ( order by <expression>)

SQLcl Introduction The Next Generation of SQL*Plus?

210 SQL*Plus • Introduced in Oracle 5 (1985) • Looks very simple but has tight integration with other Oracle infrastructure and tools • Very good for reporting, scripting, and automation • Replaced old CLI tool called … UFI (“User Friendly Interface”)

211 What’s Wrong With SQL*Plus? • Nothing really wrong with SQL*Plus – it is being updated constantly but it is missing a lot of functionality • SQL*Plus forces us to use GUI tools to complete some basic tasks • Easy to understand, a bit hard to use • Not easy for new users or developers

212 Using SQL Developer • SQL Developer is a free GUI tool to handle common database operations • Comes with Oracle client installation starting Oracle 11g • Good for development and management of databases – Developer mode – DBA mode – Modeling mode • Has a Command Line interface (SDCLI) – but it’s not interactive

213 SQL Developer Command Line (SQLcl) • The SQL Developer Command Line (SQLcl, priv. SDSQL) is a new command line interface (CLI) for SQL developers, report users, and DBAs • It is part of the SQL Developer suite – developed by the same team: Oracle Database Development Tools Team • Does (or will do) most of what SQL*Plus can do, and much more • Main focus: making life easier for CLI users • Minimal installation, minimal requirements

214 Current Status (November 2016) • Production as of September 2016 – current version: 4.2.0.16.308.0750, November 3, 2016 • New version comes out every couple of months – Adding support for existing SQL*Plus commands/syntax – Adding new commands and functionality • The team is accepting bug reports and enhancement requests from the public • Active community on OTN forums!

215 Prerequisites • Very small footprint: 16 MB • Tool is Java based so it can run on Windows, Linux, and OS/X • Java 7/8 JRE (runtime environment - no need for JDK) • No need for installer or setup • No need for any other additional software or special license • No need for an Oracle Client

216 Installing • Download from: SQL Developer Command Line OTN Page • Unzip the file • Run it

219 Connecting to the Database • When no Oracle Client - using thin connection: EZConnect connect style out of the box connect host:port/service • Support TNS, Thick and LDAP connection when Oracle home detected • Auto-complete connection strings from last connections AND tnsnames.ora

220 Object Completion and Easy Edit • Use the tab key to complete commands • Can be used to list tables, views or other queriable objects • Can be used to replace the * with actual column names • Use the arrow keys to move around the command • Use CTRL+W and CTRL+S to jump to the beginning/end of commands

221 Command History • 100 command history buffer • Commands are persistent between sessions (watch out for security!) • Use UP and DOWN arrow keys to access old commands • Usage: history history usage History script history full History clear [session?] • Load from history into command buffer: history <number>

222 Describe, Information and Info+ • Describe lists the column of the tables just like SQL*Plus • Information shows column names, default values, indexes and constraints. • In 12c database information shows table statistics and In memory status • Works for table, views, sequences, and code objects • Info+ shows additional information regarding column statistics and column histograms

223 SHOW ALL and SHOW ALL+ • The show all command is familiar from SQL*Plus – it will show all the parameters for the SQL*Plus settings • The show all+ command will show the show all command and some perks: available tns entries, list of pdbs, connection settings, instance settings, nls settings, and more!

224 Pretty Input • Using the SQL Developer formatting rules, it will change our input into well formatted commands. • Use the SQLFORMATPATH to point to the SQL Developer rule file (XML) SQL> select * from dual; D - X SQL> format buffer; 1 SELECT 2 * 3 FROM 4* dual

225 SQL*Plus Output • SQL*Plus output is generated as text tables • We can output the data as HTML but the will take over everything we do in SQL*Plus (i.e. describe command) • We can’t use colors in our output • We can’t generate other types of useful outputs (CSV is really hard for example)

226 Generating Pretty Output • Outputting query results becomes easier with the “set sqlformat” command (also available in SQL Developer) • We can create a query in the “regular” way and then switch between the different output styles: – ANSIConsole – Fixed column size output – XML or JSON output – HTML output generates a built in search field and a responsive html output for the result only

227 Generating Other Useful Outputs • We can generate loader ready output (with “|” as a delimiter) • We can generate insert commands • We can easily generate CSV output • Usage: set sqlformat { csv,html,xml,json,ansiconsole,insert, loader,fixed,default}

228 Load Data From CSV File • Loads a comma separated value (csv) file into a table • The first row of the file must be a header row and the file must be encoded UTF8 • The load is processed with 50 rows per batch • Usage: LOAD [schema.]table_name[@db_link] file_name

229 SCRIPT – Client Side Scripting • SQLcl exposes JavaScript scripting with nashorn to make things very scriptable on the client side • This means we can create our own commands inside SQLcl using JavaScript • Kris Rice’s from the development team published multiple example on his blog http://krisrice.blogspot.com/ and in GitHub, for example the autocorrect example demo.

230 Summary • There is a lot in SQL than meets the eye • Wise use of analytic queries can be good for readability and performance • Recursive queries are good replacement for the old connect by prior but a little dangerous • Oracle 12c features are really cool! • Look out for SQLcl: it’s cool and it’s going places!

231 What Did We Not Talk About? • The Model clause • Adding PL/SQL to our SQL (Oracle 12c) • Hints and other tuning considerations • The SQL reference book is 1906 pages long. We didn’t talk about most of it…

Q&A Any Questions? Now will be the time!

Zohar Elkayam twitter: @realmgic Zohar@Brillix.co.il www.ilDBA.co.il www.realdbamagic.com

Oracle Database Advanced Querying (2016)

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